Spatial smoothing (SS) is a crucial processing step
preceding statistical analyses of blood-oxygenation-level-dependent (BOLD) fMRI
data. Although previous works have shown that the optimal SS kernel size varies
both between subjects and active regions of interest (ROIs) within subjects,
the use of a single kernel size for one or many subjects is virtually
ubiquitous. A simple and computationally efficient automated algorithm based on
matched filter theory is presented that determines the SS kernel size to
maximize t-statistics for a given ROI. The results also emphasize the benefits
of locally adaptive SS techniques to optimize BOLD contrast-to-noise in
single-subject analyses.